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A Strategy To Defend Your Portfolio From Bear Markets

It is important to protect one’s portfolio from crashes like 2007-2009 where the major market indices lost more than 50%. Historically, markets have seen long 4-10 years runs of steady Bull market interspersed with shorter 1-2 year Bear markets. Most losses in a bear market come within a short span of few months. An investor playing good defense will look to time an early exit in a crash. When market is sufficiently oversold, short term bounce backs present further opportunity to make gains. An investor who remained invested during stock market crash from October 2007 to February 2009 lost more than 50% of his investment (based on SPY performance ) during that period. Similarly, between September 2000 and September 2002, fully invested investors lost ~40% ( using SPY as a benchmark ). Both bear markets wiped out 3-5 years of preceding year gains. While timing the market is a hard proposition, it is incredibly important to preserve your portfolio from a major whitewash during a crash. “Defense wins Championships” is a famous saying in football but is more aptly relevant for investors that can successfully maneuver through a bear market. NFL teams with good defense minimize points scored against them by opposition; a good portfolio needs strong defensive strategies to protect from bear market onslaught. Further just like strong defense can actually add to score by triggering turnovers, bear market presents opportunities for sizeable gains which if not exploited means missed opportunity cost. For example, investors who were too risk averse and did not participate in the post-crash rally of 2009-2011, lost out on capital appreciation opportunity of 80-90% within that 2-year period. To build a good defensive strategy, an investor needs to understand the market dynamics. The picture below best illustrates the US stock market history of bull-bear markets ( Source: Business Insider ). (click to enlarge) Key takeaways we can derive from the above picture are: The large part of this graph is dominated by long running bull markets, with most runs lasting many years or even more than a decade. During this multi-year period, the market sees steady returns with small intermittent corrections interspersed. Some examples include the bull market in 1990s, 1980s, 1950s and 1940s, all of them lasted 10+ years. Bear markets are relatively short in terms of overall duration (1-2 years), and the losses come at a much faster rate (compared to gains in bull market). For example, 2008 crash lasted 1.3 years and 2002 crash lasted 2.1 years. The longest bear market was in the 1930s and lasted close to 3 years. “Market goes up in an escalator but down in an elevator” is a famous stock market quote that can summarize the overall dynamics. Understanding the wisdom behind these select few words is important for all investors. The picture below shows 1 example of Bull-Bear cycle in SPY adjusted close graph during the 2003-2008 period ( Source: Yahoo Finance data ). Notice the steady increase in SPY for 4+ years (escalator) followed by a dramatic 1-year crash in 2008, wiping out a large part of multi-year gains. Hence the saying, market goes like an escalator and comes down like an elevator. (click to enlarge) Here is another graph that shows SPY monthly returns ( Source: Yahoo Finance data ) during the 2008 crash period. Notice even during the bear market, the bulk of losses (~-46%) came over a short 9-month period from June 2008 to February 2009. Hence the analogy of elevator coming down vertically or fast. (click to enlarge) The above historical perspective presents multiple takeaways that should influence our investing strategy. Given the long runs of Bull market, sitting out of stock market for extended period of time has significant opportunity cost of not participating in Bull rally. If one wants to protect their portfolio in the event of a crash, they need to get out of market early in a crash. However, getting out too early has risks too as it may only be a temporary dip i.e. no crash, market recovers and one has to get back in at a higher price. So timing the market exit is a balancing act between these two scenarios. Exiting out late in a Bear market can double the pain as one will take the losses but not participate in the rally that should be soon to follow. Buy and hold investors who finally give up on stocks after seeing their portfolios trounced for a year or two, have the risk of exiting out at close to bottom of crash. Building a Defensive Strategy: The above takeaways can be formulated to build a variation of Simple Moving Average (SMA) based strategy. For our example, we will use SPY as a representative market index that we play the strategy on. However, the strategy should be verifiable on most indices with varied performance. The SMA gives an overall trend of market direction that is not easily seen with day-to-day variations. So a simple strategy could be to stay long in SPY when SPY is above its say 50-day SMA and sell all holdings when SPY falls below its 50-day SMA. When SPY index is above the SMA, it is pulling the SMA upwards i.e. leading to a positive trend in index. One big drawback of SMA-based strategies is the whipsaw effect. This happens when stock dips below the SMA, we sell the index but then stocks recover, goes above SMA and we get back. Because we are selling at a lower point and then buying back again at a higher price, this leads to a loss. If this happens with large enough frequency, the strategy can lead to sizeable losses and NEGATIVE returns as compared to Buy and Hold. Since history is dominated by large bull runs interspersed with shorter bear runs, it is probably wiser to side on being long for the most part. So we assume that more often than not the market is expected to bounce back after a dip below SMA leading to whipsaw. To reduce the number of times we go out of market and whipsaw, we can use a longer duration SMA. The longer the duration, the less likely the chance of temporary short-term dips breaching SMA and giving a false sell signal. Let’s take 250-day SMA which is equivalent to 1 year in terms of trading days. Further even when SPY touches or breaches the 250-day SMA that is a major support level indicating a high chance of bounce back. So I would propose the sell SPY signal to be even lower, say when SPY has breached more than 2% below 250-day SMA. So let’s assume that we sell SPY when it’s hit more than 2% below 250-day SMA. On top of this, let’s try to take advantage of the fact that once market is sufficiently down, volatility increases and we expect to see several bounce backs from the lows. The bounce back can be temporary though as we don’t know for sure when the actual bottom is or if the bear market is close to end. To take advantage of this short-term bounce backs, we can define a lower point at SMA for market to be oversold. In this zone, we could look to do some bottom fishing by trying to do the reverse, i.e. buy SPY when SPY is below its short-term SMA, say 4-day SMA and sell it as soon as it recovers. So our strategy becomes as follows: Stay long in SPY as long as SPY is greater than -2% (say X) of its 250-day average. Sell and go in cash if it falls below X. If SPY falls below 6% (say Y) of 250-day SMA look to bottom fish. Buy SPY when it is below its 4-day SMA expecting a short term bounce back and sell as soon as it comes back above its 4-day average. These are short-term trades that take advantage of market’s volatility. Now while the thresholds pick (X and Y) may feel like magic numbers, in my test almost all combinations of X and Y where X

GLD Continues To Lose Its Shine

The price of GLD continues to fall. The recovery of the U.S. economy keeps raising the odds of a rate hike in December, which pressures down GLD. This week’s GDP and PCE reports will provide additional insight into the direction of the U.S. economy and could indirectly move the price of the fund. The weakness in the gold market has also kept down the shares of the SPDR Gold Trust ETF (NYSEARCA: GLD ). As the U.S. economy keeps showing signs of slow progress, the market raises the odds of a December rate hike. And this trend keeps pushing down the price of GLD. This week’s GDP and PCE will provide additional information about the direction of the U.S. economy. Additional strong results could drive GLD further down. This week, the second estimate of the U.S. GDP for Q3 will be released. In the first estimate, the GDP growth rate was only 1.5%, which was much lower than that in Q2. In terms of market reaction, even though the financial markets do tend to react to the progress of the U.S. GDP, the price of GLD doesn’t seem, as presented in the following chart, to have a consistent impact from the changes in GDP. (click to enlarge) (Source: BEA, Google Finance) The chart presents the relation, or lack of it, between the percent change in the price of GLD on the day the GDP report comes out and the “surprise” in the headline figure of the GDP annual growth rate – a positive percentage point indicates a better-than-expected growth rate. The dots don’t show a clear upward or downward trend to suggest a correction. The GDP growth rate tends to coincide with the movement of the U.S. dollar. And the latter also has a mid-strong correlation with the price of GLD – as the U.S. dollar rises, GLD tends to fall. But directly, GLD doesn’t seem to react in a more consistent way to the surprises of GDP’s headline figure. How about the relation over the long run? Although the slow recovery of the U.S. economy may have contributed, at least indirectly, to the decline in the price of gold, when you look at the two data series over the past decade, it’s hard to see any correlation as well. (click to enlarge) (Source: FRED ) These charts suggest that the progress of U.S. dollar, changes in long-term yields, the concerns over rising inflation and even, to a lesser extent, changes in the supply of gold are likely to be the main direct factors moving GLD. Does this mean the GDP report doesn’t matter? I don’t think so, especially at this stage when the FOMC contemplates whether or not to raise rates. After all, the market estimates that the chances for a rate hike next month by the FOMC are 74%. At the beginning of the month, these odds were lower than 50%. As the chances continue to climb, GLD tends to fall. It’s true that the Fed’s dual mandate refers to employment and price stability, not growth. These two targets are related to the GDP growth rate. And if GDP doesn’t rise, the Fed will be less incline to raise rates. Currently, the market expects GDP growth to come in higher than the first estimate of 2%. Any higher figure could indicate the U.S. economy is doing better than was previously estimated – another positive sign that could keep GLD prices down. It’s also worth noticing the components of the GDP report, such as changes in inventories, investments and personal spending. This week, we also have the PCE report – another indicator for the progress of U.S. inflation. In the past report, the core PCE stood at 1.3% year on year (as of September). The Fed’s annual outlook was 1.4% . If the core PCE doesn’t start to pick up, the Fed expects next year’s core PCE will rise to 1.7%. This may not delay the Fed from raising rates in December, but rather, it may maintain a very gradual rate hike pace next year, which will actually keep interest rates low and GLD from crashing. The price of GLD is still likely to further slowly decline. This week’s reports will provide additional information about the progress of the U.S. economy. As the U.S. dollar and long-term interest rates continue to climb, the downward pressure on GLD will intensify. It doesn’t mean the fund couldn’t experience short-term rallies – especially if the risk in the financial markets rises or the U.S. economy doesn’t progress or U.S. dollar changes course again and depreciates, just to name a few factors. But these short-term rallies aren’t likely, for now, to change the fact of the descent of GLD. For more please see ” GLD Continues to lose its appeal ”

Bring More Data

Several months ago we posted an article called ” Bring Data ” where we showed the importance of having abundant data for system development and validation. This was further reinforced to us recently when someone actually brought us additional U.S. stock sector data. Previously, we only had Morningstar sector data that went back to 1992, which we used to construct our Dual Momentum Sector Rotation (DMSR) model. (S&P sector data also goes back to only the early 1990s.) DMSR was shown in my book as one example of other ways you might use dual momentum. When we were given equivalent Thompson Reuters U.S. stock sector data back to 1973, we immediately extended our DMSR back test to include this additional data. After incorporating the new data, DMSR still looked considerably more attractive than buying and holding the S&P 500 index. But one could argue that the performance of models using broad-based equity indexes, such as Global Equities Momentum (GEM), now looks better than DMSR. Here are the comparative performance figures from January 1974 through October 2105: GEM DMSR S&P 500 Average Annual Return 17.36 15.86 12.21 Standard Deviation 12.32 14.55 15.43 Sharpe Ratio 0.89 0.67 0.42 Maximum Drawdown -17.84 -33.96 -50.95 Results are hypothetical, are NOT an indicator of future results, and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Please see our website’s Performance and Disclaimer pages for more information. Because the monthly correlation between GEM and DMSR is only 0.59, sector rotation can still have a useful but modest role to play in a diversified equities-oriented portfolio. But DMSR is not the best choice as a core portfolio holding. Sector rotation programs that use data no further back than the early 1990s to develop their models may be in for a rude awakening someday if future drawdowns are higher and returns are lower than they expect based on back testing with a limited amount of data. Along the same lines, there are also momentum-based portfolios popping up on the internet all the time now, some even labeled as “dual momentum,” that are modeled on the basis of only 10 or 15 years of ETF data. Momentum may be robust enough that future results won’t suffer much because of this. But those who think they are constructing optimal models this way are just fooling themselves. Overfitting modest amounts of data is one of the most pernicious problems in the development of investment models. Those who do this may argue that the markets change over time, so the best model parameters from years ago may not be as relevant as today’s best parameters. This may be true. However, what is also true is that today’s parameter values are also likely to be sub-optimal when moving forward in time. The following chart from my book, Dual Momentum Investing , shows what I mean: Chart courtesy of Tony Cooper The S&P 500 is highlighted in different colors for each 15 year period. You can see that the latest period, 1999-2013, looks different from the preceding period, 1984-1998. 1999-2013, in fact, looks more like the earlier 1969-1983 period. 1984-1998 is also different from its preceding period, 1969-1983 and similar to the earlier years 1954-1968. If you had used each 15-year period to develop your model, you would have had something unsuited for each of the next 15-year periods. You would likely be better off using all four periods to formulate a model rather than just the last 15-year period. The more data you use, the more likely you are to have a robust model that will hold up reasonably well in the future, even though it isn’t the best fit to any one particular period. The 12-month look back parameter we use for our GEM and ESGM dual momentum models was found to work well in 1937 by Cowles & Jones . It has been used extensively in momentum research since then and has held up well out-of-sample. But there is a lot more history than that to help give us more confidence in momentum. Let’s take a look at some of that now. We focus on stocks as our core asset since they have historically offered the highest risk premium to investors. U.S. stocks, in particular, have given investors the best long-run returns. Other assets can create a drag on long-run portfolio performance. They also lose some importance as diversifiers once you use a trend following overlay like absolute momentum to help attenuate your downside risk exposure. The longest back test on stock market momentum is by Geczy and Samonov (G&S). Their 2013 paper called ” 212 Years of Price Momentum: The World’s Longest Back Test 1801-2012 ” compared the top one-third to the bottom one-third of U.S. stocks sorted monthly by relative momentum. Over this entire sample period, the top equally weighted momentum stocks outperformed the bottom ones by 0.4% per month with a highly significant t-stat of 5.7. Prior to this study, momentum outperformance on U.S. stocks had been found significant back to 1926. G&S showed that stock momentum was also positive and statistically significant from 1801 to 1926. G&S also found that stock market momentum was remarkably consistent. In only 2 of the 21 decades from 1801 through 2012 did long-only momentum under perform buy-and- hold, and these were by just -1.2% and -0.7% annually. In all the other 19 decades, momentum outperformed buy-and-hold by an average of 3.8% annually. This year G&S came out with a new study called, ” 215 Years of Global Multi-Asset Momentum: 1800-2014: Equities, Sectors, Currencies, Bonds, Commodities, and Stocks .” Here G&S expanded their momentum study to cover six different asset classes, including bonds, stock sectors, and equity indices, which are the ones we use in our momentum models. [1] G&S demonstrated the outperformance of momentum inside and across all asset classes except commodities. Here is a chart from their paper showing the log cumulative equally weighted average of the 6 asset classes plus the cross asset momentum excess returns. The strongest momentum effect is in country equity indices, which had a long-only monthly excess return over buy-and-hold of 0.52% with a highly significant t-stat of 11.7, compared to 0.29% with a t-stat of 6.4 for individual U.S. stocks and 0.24% with a t-stat of 15.5 for all assets. G&S also show that long-only absolute (time series) momentum outperformed buy-and-hold by 0.15% per month with a t-stat of 11.2. For those who want to further their momentum education, I suggest you first read the seminal paper by Jegadeesh and Titman (1993) that started the modern momentum renaissance. Next, learn about absolute momentum from Moskowitz et al (2012) or Antonacci (2013). Then follow up with Geczy and Samonov (2015) to satisfy yourself as to the efficacy and robustness of momentum investing based on 215 years of empirical evidence. [1] Equity indexes are equally as good as individual stocks (or better, according to G&S) in capturing the momentum effect. Indexes are much easier to use and avoid the enormously high transaction costs associated with rebalancing momentum-based stock portfolios.